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What do we mean when we talk about Data & Knowledge? The terms are two sides of the same coin. From data, we can derive knowledge and without data there will be no knowledge. Data marks our starting point and knowledge is where we want to end up.
The Networked Society with its 50 billion or so connected devices offers a great opportunity to explore Data & Knowledge. In fact, the Network Society will be built on a solid understanding on how we can turn data into something meaningful.
Still, creating knowledge is not necessarily simple and typically it involves a number of steps that you need to go through. We organize these into three distinct, and equally important, activities.
Handling Data. Before you can analyze your data, it needs to be collected and stored in such a way that we can access it. In our case, it means being able to store very large amounts of network data, removing noise from the data, and above all being able to handle data that is generated at an extremely high rate. To this end, we investigate Big Data technologies such as Hadoop and the distributed stream engine S4.
Doing Analytics. Once we have a good representation of our data we can analyze it to find the hidden patterns it contains. This is the basic building block to turn data into knowledge. In this area, we are very focused on machine learning, social network analysis and data mining to make predictions about what is to come. A simple example of this is: to predict where in a network you will have increased traffic at a certain time.
Creating Insights. The final step in our knowledge building process is to get actual insights. If our predictions are not well understood by the person who bases decisions on them, we have gained very little knowledge. Thus, as much as Data & Knowledge is about the raw crunching of data, we have to consider the human aspect as well. How can large amounts of data be visualized? How can one quickly and efficiently interact and work with data? How can we explain the reasoning process behind a prediction?
In this Research Topic, we will address this entire process from handling data to creating insights. We will touch on privacy and security, which always has to be considered when working with data. We will inevitably explore Big Data, data visualization and provide various proofs-of-concepts thereof.